Coping in the final frontier: An intervention to reduce spaceflight-induced stress
Bibliographic record
Abstract
Research in human spaceflight has extensively documented how microgravity environments, such as spaceflight across Low Earth Orbit (LEO), affects astronauts’ and Spaceflight Participants’ emotions. However, a more refined understanding of this topic will become especially relevant as national and international space agencies increase the duration of manned space missions, and as the private sector fully enters the aerospace arena. In this paper, we analyze the strengths and weaknesses of the four main types of interventions for dealing with the stressors associated with human spaceflight (i.e., ergonomic, physiological, psychological, and psychosocial), and then elaborate on a psychosocial intervention grounded on evidence-based interventions across several fields of psychological research. Among the components of such interventions, we recommend adopting advanced stress coping strategies, developing emotional and intercultural competencies and crafting a shared social identity among crew members. Our proposed intervention aims to enhance the efficacy of social support as a key coping mechanism and applies to crewmembers and spaceflight participants of diverse cultural backgrounds who, most likely, will work using computer-mediated communication (CMC).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".